Regularization for high-dimensional covariance matrix

• Autorzy: Xiangzhao Cui , Chun Li , Jine Zhao , Li Zeng , Defei Zhang , Jianxin Pan
• Języki publikacji: EN

Abstrakty

EN

In many applications, high-dimensional problem may occur often for various reasons, for example, when the number of variables under consideration is much bigger than the sample size, i.e., p >> n. For highdimensional data, the underlying structures of certain covariance matrix estimates are usually blurred due to substantial random noises, which is an obstacle to draw statistical inferences. In this paper, we propose a method to identify the underlying covariance structure by regularizing a given/estimated covariance matrix so that the noises can be filtered. By choosing an optimal structure from a class of candidate structures for the covariance matrix, the regularization is made in terms of minimizing Frobenius-norm discrepancy. The candidate class considered here includes the structures of order-1 moving average, compound symmetry, order-1 autoregressive and order-1 autoregressive moving average. Very intensive simulation studies are conducted to assess the performance of the proposed regularization method for very high-dimensional covariance problem. The simulation studies also show that the sample covariance matrix, although performs very badly in covariance estimation for high-dimensional data, can be used to correctly identify the underlying structure of the covariance matrix. The approach is also applied to real data analysis, which shows that the proposed regularization method works well in practice.

Słowa kluczowe

EN

High-dimensional; Covariance estimation; Covariance structure; Regularization

• Wydawca: De Gruyter Open
• Czasopismo: Special Matrices
• Rocznik: 2016
• Tom: 4
• Numer: 1
• Strony: 189-201

Daty

otrzymano

2015-10-09

zaakceptowano

2016-03-28

online

2016-04-12

Twórcy

autor

Xiangzhao Cui

• School of Mathematics, University of Honghe, Yunnan, China

autor

Chun Li

• School of Mathematics, University of Honghe, Yunnan, China

autor

Jine Zhao

• School of Mathematics, University of Honghe, Yunnan, China

autor

Li Zeng

• School of Mathematics, University of Honghe, Yunnan, China

autor

Defei Zhang

• School of Mathematics, University of Honghe, Yunnan, China

autor

Jianxin Pan

• School of Mathematics, University of Manchester, UK

Bibliografia

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Identyfikatory

DOI

10.1515/spma-2016-0018